Modeling and prediction of third-party claim using a Machine learning approach

Abstract

Background/Objectives: The main objective of this research paper is to build an appropriate mathematical model that helps in forecasting overall claim amount based on the chosen characteristics of the data. Methods/Statistical analysis: In the field of actuarial research, forecasting the third-party claim amount for Motor vehicles is a challenging task, and only limited empirical research studies are done in predicting the claim. In this context, the annual time series historical claim data were collected for a period of 34 years to examine the predictive performance of the linear regression model, exponential smoothing model, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and hybrid ARIMA-ANN models to predict third party claim amount of motor insurance data in India. Findings: The data are analyzed, and the empirical evidence from the study shows that the ANN model improved the accuracy prediction when compared to Linear Regression, Exponential smoothing model, ARIMA and a hybrid model with respect to the performance criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Therefore, the ANN model is more potent in forecasting TP claim amounts by considering the adequacy, suitability, and accuracy of the data modeling. Novelty/Applications: This data analytics approach would help motor insurance companies in India to have an idea about the expected future claim amounts. Also, this modeling approach will help the Motor Insurance companies of India to provide a better customer-centric forecasting model, which ensures better claims settlement and management. Keywords: Claim amount; linear model; stationarity; ARIMA; neural network; TRAINLM